IBM Netezza to Synapse Migration – 2 Hr Workshop

Hexaware Technologies

In this Value Discovery Workshop learn how you can cut down on IBM Netezza to Azure Synapse migration cost & timelines via a risk mitigated approach using Hexaware’s AMAZE®.

Why Azure Synapse Migration :

Legacy Data Warehouses such as IBM Netezza have a fixed compute and storage ability, which prohibits enterprises from realizing their true business potential. It impacts scalability and cripple improvements due to high maintenance cost. By migrating to Azure Synapse, enterprises can overcome these challenges, gather insights at incredible speed and experience highest price-performance.

However, migrating to cloud itself can be cost-intensive, which typically eats into the ROI. Moreover, the risks associated with migrating to cloud may delay the process. Enterprise therefore value a solution that is able to avoid these pitfalls.

Hexaware’s Solution :

Hexaware’s AMAZE® carries out cost-effective re-platforming of entire data warehouse landscape to Synapse in a secure and timely manner. It ensures that the data is encrypted at rest as well as in transit. And moreover, for data reconciliation and performance optimization it carries out data and performance testing respectively – at both pre as well as post migration stage.

Its capabilities include :

  • Re-platforming IBM Netezza to Synapse.
  • Re-platforming or Repointing ETL tool to Azure Data Factory or Synapse resp.
  • Re-platforming or Repointing Reporting tool to Power BI or Synapse resp.

Agenda :

  • Hexaware Overview
  • Current & Target Technology State
  • Assessment & Migration Plan
  • Commercials
  • Relevant Proof-points
  • Q&A

Objective :

Provide an estimate on the following :

  • Cost of migration.
  • Migration timeline.
  • TCO savings.
  • Risk mitigated approach for a successful migration.

Who should participate?

CXO, VP and Director level stakeholders in Data Warehouse Ecosystem.

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https://store-images.s-microsoft.com/image/apps.34912.b23d800f-4b31-412a-86f8-bae8ecab6940.0e39e962-fc84-411a-8f24-6908e1e44ec3.71e6f983-23de-416f-8241-168e192827e9
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